Hybrid Beamforming for Massive MIMO - A Survey
Andreas F. Molisch, Vishnu V. Ratnam, Shengqian Han, Zheda Li, Sinh Le, Hong Nguyen, Linsheng Li, Katsuyuki Haneda

TL;DR
This survey reviews hybrid beamforming techniques for massive MIMO systems, focusing on their architectures, CSI requirements, hardware complexities, and millimeter-wave operation considerations.
Contribution
It provides a comprehensive taxonomy and analysis of hybrid beamforming structures, highlighting their advantages and design challenges in massive MIMO.
Findings
Different CSI adaptation strategies impact performance and overhead.
Hardware complexity varies across different hybrid structures.
Millimeter-wave operation requires specialized design considerations.
Abstract
Hybrid multiple-antenna transceivers, which combine large-dimensional analog pre/postprocessing with lower-dimensional digital processing, are the most promising approach for reducing the hardware cost and training overhead in massive MIMO systems. This paper provides a comprehensive survey of the various incarnations of such structures that have been proposed in the literature. We provide a taxonomy in terms of the required channel state information (CSI), namely whether the processing adapts to the instantaneous or the average (second-order) CSI; while the former provides somewhat better signal-to-noise and interference ratio (SNIR), the latter has much lower overhead for CSI acquisition. We furthermore distinguish hardware structures of different complexities. Finally, we point out the special design aspects for operation at millimeter-wave frequencies.
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsAdvanced MIMO Systems Optimization · Millimeter-Wave Propagation and Modeling · Microwave Engineering and Waveguides
